Revvity, Inc., has introduced its upcoming Signals Xynthetica Models-as-a-Service AI platform within the Revvity Signals ecosystem. This new platform is designed to enhance molecular and materials discovery by combining AI-driven design, predictive modeling, and experimental validation in a single, governed environment. Scientific teams will be able to iteratively design, test, and refine candidate molecules with greater confidence, bridging the gap between in-silico predictions and real-world laboratory results.
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In life sciences and materials research, artificial intelligence is transforming discovery from traditional trial-and-error methods to more predictive, data-driven approaches. However, the impact of AI is limited if it is not tightly integrated with high-quality experimental data and the workflows scientists use daily. Signals Xynthetica addresses this challenge by embedding predictive models directly into the scientific context, connecting AI-generated insights with real laboratory outcomes to support continuous learning and improvement.
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The platform supports various in-silico design approaches, including de novo molecular generation, property prediction, and multi-objective optimization. Delivered as a Models-as-a-Service solution, it allows organizations to access and apply predictive models without investing in complex AI infrastructure. Models can be consistently applied across projects, updated as new data becomes available, and evaluated transparently alongside experimental results.
Kevin Willoe, President of Revvity Signals Software, highlighted the importance of connecting AI with practical scientific workflows. He said that Signals Xynthetica is designed to operationalize predictive science, combining computational design with experimental validation in a continuous feedback loop that accelerates discovery across industries.
Signals Xynthetica is expected to play a central role in Revvity’s broader AI-augmented discovery strategy. The platform aims to help customers leverage predictive capabilities at scale while ensuring strong governance, data stewardship, and scientific rigor. Pre-registration for the platform is now open, and early access programs are scheduled to begin in the first half of 2026.
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